TechnologyArtificial Intelligence in the field ofBuilding Automation
Professor Michael Krödel, CEO, Institute of Building
Technology, Ottobrunn, Germany and Professor for Building
Automation and Technology, University of Applied Sciences at
Rosenheim and Graham Martin Chairman & CEO, EnOcean
Alliance
The term “AI – Artificial Intelligence“ is increasingly associatedwith buildings and building automation. The question is: what
is it, where do its tangible benefits lie in this field, and how
does the building infrastructure need to be adapted to realise
those benefits?
Today’s building automation systems in the main operate‘statically’ in response to fixed time programs or simple control
parameters. Room temperature control is based on a preset
temperature that is the same throughout the day. Lighting isoperated manually, with switches, or on the basis of simple
presence switches. None of this is truly ‘intelligent.’ The new
dimension that AI can add into the building automation
environment is to use autonomous analysis of the data as abasis for optimised operation. Thus the heating and coolingdynamic of rooms, weather forecasts, predicted room
occupancy during the course of the day can all be factored into
the operation of the heating. Similarly, cleaning schedules canbe based not only on the current actual values in terms of theintensity of use of kitchens, canteens and toilets and otherareas, but can be based on predictions drawn from an analysis
of usage patterns in the previous days and weeks. This kind of
forward looking building management can be applied in almost
every area of building services, leading to increased energy
efficiency, reduced operating costs, improved space utilisationand other advantages.
All this - and much more - is possible when data on building
system status and conditions is intelligently evaluated. Thisrequires intensive processing of large amounts of data, withmany variables to be considered. Artificial Intelligence (AI)offers many new, tailor-made solutions which are eminently
suited to efficient building management.
“Building Automation”, “Smart Building” and “CognitiveBuilding”
Initially, „Building Automation“ was comparatively „unintelligent“. Systems were programmed to follow a set ofsimple rules, allowing for quick system start-up andsubsequent ease of maintenance.
The „Smart Building“ typically builds on this classic building
automation with flexible IT-based management systems . These
offer unrestricted programming using modern IT languages andtools, easy integration with other IT systems such asworkspace/room reservation systems or data banks, and datavisualisation for facility managers and for „ordinary“ users.
The growing assimilation of sensor-generated data into the ITbased management level opens the way for more advanced
data processing solutions to come into play - such as AI tools.
This is the pre-condition for the implementation of any
prognosis-based form of building management. The
sophisticated processing of sensor-generated data makes theSmart Building into a „Cognitive Building“.

20AI-learning process
The first step in any Artificial Intelligence process is system
learning. This can take three forms.

Unsupervised Learning
Supervised Learning
Reinforcement Learning„Unsupervised Learning“ is used when large quantities ofdata must be processed and categorised. This grouping
enables the recognition of deviations from norms andinterdependencies. For example, sensor data from identicalcirculation pumps can be grouped. If data from one pump orgroup of pumps deviates from the norm, there may be adefect and a human engineer can be sent to investigate.
„Supervised Learning“ often makes use of neural networks.
They consist of entry and exit nodes as well as further nodes
in the intermediate layers. Mathematically weighted
relationships exist between the diverse nodes (neurons). In
order to optimise these relationships, the neural network is
subjected to a training phase with known input and output
patterns. In the field of building automation, for example, aneural network can ‚learn‘ the current consumption profiles ofdifferent appliances and which appliances are active when. This
information can be used to avoid ‚spikes‘ in building energy
consumption, by shutting down some appliances and extending
the operation time of others.
Another form of Artificial Intelligence is represented byprocesses that autonomously determine which actions areTechnology

appropriate in a given situation. They emulate humanbehaviour whereby different solutions are tried in order todetermine the best way forwards in a hitherto unknown
situation, and conclusions drawn retrospectively. The learning
task becomes more challenging when feedback is given muchlater and hinges upon events in the relatively distant past. Thisis true in a human context, and equally true in computer
environments. The best-known example in this category is„Reinforcement Learning“. Consider the issue of determining
the optimal start and stop times of heating to achieve a
comfortable temperature when the building opens. At thesimplest level, the learning algorithm receives the value fromthe room temperature sensor and can act on the actuator on
the radiator. By a process of trial and error, the algorithm candetermine the necessary lead time. However, this simple
example ignores the fact that, for instance, the speed ofheating also depends on the outside temperature, so thereading from an exterior temperature sensor needs to beconsidered. Instead of providing a pre-set target temperature,
the algorithm may be be given evaluations (good / OK / cold)
during the day and must learn in response to this feedback. In
addition, the algorithm can be provided with an addditional
rating every month based on the overall energy cost:encouraging efficient behaviour and discouraging inefficientresponses. A ‚stable‘ response that balances comfort andefficiency can be established, but exploration should continue
to accomodate changes in behaviour and the environment.

meeting rooms, canteens, pantries, transit areas, toilets andparking spaces as well as the provision of information in the
short term (for building occupants) and in the long term (forfacility managers, e.g. in form of advice on building
restructuring).

It can be seen that these three approaches arecomplementary. The learning method should be chosendepending on the task in hand - each has its merits.

An AI platform is indispensable for the introduction of
intelligent learning processes such as those described above.
This can be either cloud-based or server-based. Cloud-based
server farms offer more processing power, and cloud-based AI
frameworks offer a broader range of features, so this currently
represents common practice.

Concrete applications
Many diverse AI-based applications are available in the fieldof building automation. They can be broadly categorised asfollows:

Optimised facility management: needs-based control ofheating plants, circulating pumps, lighting etc. (as opposed tocontrol on the basis of simple parameters or by timer).
Optimised utilisation of spaces and infrastructure:
capacity analysis and forecasting, e.g. for

Load management: forward-looking operation of electricalsystems in order to avoid (costly) peak loads.
Precautionary maintenance and optimised servicing:analysis of failure probability, timely maintenance andconsequential avoidance of technical failures.

Employee-oriented value added services: mobile devices
can - for instance - be used to generate space utilisation
forecasts, view canteen usage intensity, request parking space
availability and preferred workspace location or selectindividual meals.
Compensation of skilled-staff shortages: making effective
use of facility maintenance staff in managing the building’s
technical systems.
Focus on meaningful sensor data: generate as much dataas possible from as few sensors as possible - reducing
redundancy, cutting investment and operating costs.

Demands upon system architecture

The AI platform is built on a Smart Building infrastructure,
and all technical systems should ideally be connected to aBMS (Building Management System). The BMS must be able togovern the building facility and room automation systems.

Demands on building infrastructure
The AI platform requires a rich set of data from a variety ofsensors around the building to operate effectively. Conventional
smart building systems use a sensor network to determine the21Technology

status of a building now.‘ Cognitive“ buildings store andanalyse historical sensor data to make predictions for thefuture. For this reason, such buildings are even more criticallydependent on the data inputs they receive for their success.
Cognitive buildings need to be instrumented throughout with
IoT sensor devices that make the algorithms fully aware ofevery aspect of their operation: environment, occupants, energy
requirements, service needs, security, and safety. The richer thedata, the more complete and intelligent the response of the AI.
Wiring sufficient sensors into an established building is hugely
expensive – and even if it were done would create an inflexible
architecture that couldn’t be adapted as new applications
emerge and learning progresses. The only effective solution isbattery- and maintenance- free energy harvesting sensors thatcan be fitted in a moment and moved at will. Energy
harvesting wireless devices utilize the tiniest amounts ofenergy from their environment. Kinetic motion, pressure, light,
differences in temperature are converted into energy which, in
combination with ultra-low power wireless technology, creates
maintenance-free sensor solutions for use in smart buildings
and the IoT.
The EnOcean Alliance eco-system offers more than 5,000
multi-vendor interoperable energy-harvesting sensors enablingdata collection for multiple applications, such as room ordesk/chair occupancy, temperature and air quality, energy usage
and restroom usage. In addition to the traditional option ofcollecting and analysing the data via the BMS (BuildingManagement System), this can now be done by using theexisting Wi-Fi network with the building. By securelyinterfacing those IoT devices with new and existing Aruba Wi-Fi
5 and Wi-Fi 6 Access Points via a plug-in 800/900MHz radio,building control and business applications can become
hyperaware of their operating environments. This information
can be used to better model building behavior, to optimize
human activity monitoring, organizational redesign, augmented
reality, human productivity, and occupant health and safety.

Conclusions
AI-based processes enable a broad range of applications in
the field of building automation. The concrete benefits
anticipated from AI-based solutions should be clearly defined
before implementation, since this plays a determining role inthe choice of learning process and its modelling, as well as in
the choice of AI platform and the type, number and location of
the energy harvesting sensors needed to supply the datainputs.
www.igt-institut.de
www.enocean-alliance.org